{ "cells": [ { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import plotly.express as px\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from fbprophet import Prophet" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCountryConfirmedRecoveredDeaths
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342020-02-25Afghanistan100
352020-02-26Afghanistan100
362020-02-27Afghanistan100
372020-02-28Afghanistan100
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" ], "text/plain": [ " Date Country Confirmed Recovered Deaths\n", "33 2020-02-24 Afghanistan 1 0 0\n", "34 2020-02-25 Afghanistan 1 0 0\n", "35 2020-02-26 Afghanistan 1 0 0\n", "36 2020-02-27 Afghanistan 1 0 0\n", "37 2020-02-28 Afghanistan 1 0 0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_url=\"https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv\"\n", "df=pd.read_csv(dataset_url)\n", "df = df[df['Confirmed']>0]\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "geo": "geo", "hovertemplate": "Date=2020-02-24
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Country=%{location}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.choropleth(df,locations=\"Country\",locationmode='country names',color='Recovered',animation_frame='Date')\n", "fig.update_layout(title_text='Global Recovered of COVID-19')\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "def visualizer(df,lockdown_start_date,lockdown_end_date,cases):\n", " print(\"VISUALIZER\")\n", " print()\n", " print(df.head())\n", " print()\n", " print(df.tail())\n", " st = \"\"\n", " if cases == \"Confirmed\":\n", " st = \"Infection Rate\"\n", " if cases == \"Deaths\":\n", " st = \"Death Rate\"\n", " if cases == \"Recovered\":\n", " st = \"Recovered Rate\"\n", " df[st] = df[cases].diff()\n", " df = df[df[st]>0]\n", " \n", " fig=px.line(df,x=\"Date\",y=st)\n", " fig.add_shape(dict(type=\"line\",x0=lockdown_start_date,y0=0,x1=lockdown_start_date,y1=df[st].max(),line=dict(color=\"red\",width=2)))\n", " fig.add_annotation(dict(x=lockdown_start_date,y=df[st].max(),text='starting date of the lockdown'))\n", " fig.add_shape(dict(type='line',x0=lockdown_end_date,y0=0,x1=lockdown_end_date,y1=df[st].max(),line=dict(color=\"red\",width=2)))\n", " fig.add_annotation(dict(x=lockdown_end_date,y=df[st].max(),text=\"lockdown end date\"))\n", " fig.show()\n", " \n", " print(\"Maximum \"+st+\" till now is \",df[st].max())\n", " df = df.dropna()\n", " df = df.drop(st,axis=1)\n", " df.groupby(\"Country\").sum().plot.barh()\n", " " ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def predictor(df,cases):\n", " print(\"PREDICTOR\")\n", " df = df.sort_values('Date')\n", " df = df[df[cases]>0]\n", " prophet_df = df[['Date',cases]]\n", " print(prophet_df.head())\n", " prophet_df = prophet_df.rename(columns={'Date':'ds',cases:'y'})\n", " m = Prophet()\n", " m.fit(prophet_df)\n", " future = m.make_future_dataframe(periods=365)\n", " forecast = m.predict(future)\n", " m.plot(forecast,xlabel='Date',ylabel=cases)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter country nameIndia\n", "lockdown start date2020-03-24\n", "lockdown end date2020-08-17\n", "Enter case(Confirmed,Deaths or Recovered)Confirmed\n", "VISUALIZER\n", "\n", " Date Country Confirmed Recovered Deaths\n", "19679 2020-01-30 India 1 0 0\n", "19680 2020-01-31 India 1 0 0\n", "19681 2020-02-01 India 1 0 0\n", "19682 2020-02-02 India 2 0 0\n", "19683 2020-02-03 India 3 0 0\n", "\n", " Date Country Confirmed Recovered Deaths\n", "19915 2020-09-22 India 5562663 4497867 88935\n", "19916 2020-09-23 India 5646010 4587613 90020\n", "19917 2020-09-24 India 5732518 4674987 91149\n", "19918 2020-09-25 India 5818570 4756164 92290\n", "19919 2020-09-26 India 5903932 4849584 93379\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":14: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Date=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Infection Rate till now is 97894.0\n", "PREDICTOR\n", " Date Confirmed\n", "19679 2020-01-30 1\n", "19680 2020-01-31 1\n", "19681 2020-02-01 1\n", "19682 2020-02-02 2\n", "19683 2020-02-03 3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n", "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "input_df = input(\"Enter country name\")\n", "lsd = input(\"lockdown start date\")\n", "led = input(\"lockdown end date\")\n", "cases = input(\"Enter case(Confirmed,Deaths or Recovered)\")\n", "visualizer(df[df[\"Country\"]==input_df],lsd,led,cases)\n", "predictor(df[df[\"Country\"]==input_df],cases)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 4 }